Does supply always come on the heels of demand? Matches and mismatches in e-learning

Developing sustainable e-learning requires a better understanding of the perceptions and preferences of e-learning providers and e-learners on the four crucial dimensions for elearning success including pedagogies, technologies, learning resources and management of learning resources. There is, however, little research on evaluating whether these critical dimensions are perceived as critical by e-learning providers and e-learners. To address this issue, this study investigates the gap between e-learners’ and e-learning providers’ perceptions and preferences on these critical dimensions for e-learning effectiveness. Such an investigation paves the way for developing appropriate measures to reduce the gap between the supply and the demand for sustainable e-learning.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.